Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy.
Deep learning
Prostate cancer
Segmentation
Ultrasound
magnetic resonance imaging–transrectal ultrasound fusion biopsy
Journal
European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
23
01
2019
revised:
25
03
2019
accepted:
10
04
2019
pubmed:
28
4
2019
medline:
29
3
2022
entrez:
28
4
2019
Statut:
ppublish
Résumé
Although recent advances in multiparametric magnetic resonance imaging (MRI) led to an increase in MRI-transrectal ultrasound (TRUS) fusion prostate biopsies, these are time consuming, laborious, and costly. Introduction of deep-learning approach would improve prostate segmentation. To exploit deep learning to perform automatic, real-time prostate (zone) segmentation on TRUS images from different scanners. Three datasets with TRUS images were collected at different institutions, using an iU22 (Philips Healthcare, Bothell, WA, USA), a Pro Focus 2202a (BK Medical), and an Aixplorer (SuperSonic Imagine, Aix-en-Provence, France) ultrasound scanner. The datasets contained 436 images from 181 men. Manual delineations from an expert panel were used as ground truth. The (zonal) segmentation performance was evaluated in terms of the pixel-wise accuracy, Jaccard index, and Hausdorff distance. The developed deep-learning approach was demonstrated to significantly improve prostate segmentation compared with a conventional automated technique, reaching median accuracy of 98% (95% confidence interval 95-99%), a Jaccard index of 0.93 (0.80-0.96), and a Hausdorff distance of 3.0 (1.3-8.7) mm. Zonal segmentation yielded pixel-wise accuracy of 97% (95-99%) and 98% (96-99%) for the peripheral and transition zones, respectively. Supervised domain adaptation resulted in retainment of high performance when applied to images from different ultrasound scanners (p > 0.05). Moreover, the algorithm's assessment of its own segmentation performance showed a strong correlation with the actual segmentation performance (Pearson's correlation 0.72, p < 0.001), indicating that possible incorrect segmentations can be identified swiftly. Fusion-guided prostate biopsies, targeting suspicious lesions on MRI using TRUS are increasingly performed. The requirement for (semi)manual prostate delineation places a substantial burden on clinicians. Deep learning provides a means for fast and accurate (zonal) prostate segmentation of TRUS images that translates to different scanners. Artificial intelligence for automatic delineation of the prostate on ultrasound was shown to be reliable and applicable to different scanners. This method can, for example, be applied to speed up, and possibly improve, guided prostate biopsies using magnetic resonance imaging-transrectal ultrasound fusion.
Sections du résumé
BACKGROUND
Although recent advances in multiparametric magnetic resonance imaging (MRI) led to an increase in MRI-transrectal ultrasound (TRUS) fusion prostate biopsies, these are time consuming, laborious, and costly. Introduction of deep-learning approach would improve prostate segmentation.
OBJECTIVE
To exploit deep learning to perform automatic, real-time prostate (zone) segmentation on TRUS images from different scanners.
DESIGN, SETTING, AND PARTICIPANTS
Three datasets with TRUS images were collected at different institutions, using an iU22 (Philips Healthcare, Bothell, WA, USA), a Pro Focus 2202a (BK Medical), and an Aixplorer (SuperSonic Imagine, Aix-en-Provence, France) ultrasound scanner. The datasets contained 436 images from 181 men.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Manual delineations from an expert panel were used as ground truth. The (zonal) segmentation performance was evaluated in terms of the pixel-wise accuracy, Jaccard index, and Hausdorff distance.
RESULTS AND LIMITATIONS
The developed deep-learning approach was demonstrated to significantly improve prostate segmentation compared with a conventional automated technique, reaching median accuracy of 98% (95% confidence interval 95-99%), a Jaccard index of 0.93 (0.80-0.96), and a Hausdorff distance of 3.0 (1.3-8.7) mm. Zonal segmentation yielded pixel-wise accuracy of 97% (95-99%) and 98% (96-99%) for the peripheral and transition zones, respectively. Supervised domain adaptation resulted in retainment of high performance when applied to images from different ultrasound scanners (p > 0.05). Moreover, the algorithm's assessment of its own segmentation performance showed a strong correlation with the actual segmentation performance (Pearson's correlation 0.72, p < 0.001), indicating that possible incorrect segmentations can be identified swiftly.
CONCLUSIONS
Fusion-guided prostate biopsies, targeting suspicious lesions on MRI using TRUS are increasingly performed. The requirement for (semi)manual prostate delineation places a substantial burden on clinicians. Deep learning provides a means for fast and accurate (zonal) prostate segmentation of TRUS images that translates to different scanners.
PATIENT SUMMARY
Artificial intelligence for automatic delineation of the prostate on ultrasound was shown to be reliable and applicable to different scanners. This method can, for example, be applied to speed up, and possibly improve, guided prostate biopsies using magnetic resonance imaging-transrectal ultrasound fusion.
Identifiants
pubmed: 31028016
pii: S2405-4569(19)30125-7
doi: 10.1016/j.euf.2019.04.009
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
78-85Informations de copyright
Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.